Dianne Cook
Kevin Wang
Priya Dingorkar
Shabarish Sai Subramanian
Gwan Ru Chen
A potent tool designed to simplify the study of data from the OECD’s Programme for International Student Assessment (PISA) is the learningtower R package. This international evaluation, which focusses on the reading, arithmetic, and science skills of 15-year-old pupils, gathers data from more than 70 countries every three years. Researchers can easily access these statistics from 2000 to 2022 using the learningtower package, which makes it easier to examine trends in educational outcomes, student performance, and contextual factors like socioeconomic status. Learningtower facilitates more effective and efficient cross-country and longitudinal evaluations of educational systems around the world by streamlining the process of managing big, complicated datasets.
The learningtower package’s 2022 version is currently being updated to guarantee compatibility with the most recent PISA data and improved features. These enhancements are intended to assist scholars in carrying out more thorough comparative analyses, providing insights into how education is changing in different nations. The package is a useful tool for researchers, educators, and policy makers who are interested in educational performance and the factors that influence it because of its easy-to-use features, which help identify important patterns and linkages.
Every three years, data from more than 70 nations is gathered for the PISA (Programme for International Student Assessment), which focusses on the academic skills of 15-year-old children. Reading, maths, and science are the three main subjects in which PISA assesses student achievement using standardised examinations. These tests give a quick overview of students’ abilities and knowledge, which serves as a foundation for comparing educational results between nations. International benchmarking made possible by PISA data assists nations in evaluating their educational systems and pinpointing areas where student learning needs to be improved.
PISA collects a wealth of contextual information in addition to the examinations by distributing questionnaires to principals, teachers, and students. Numerous facets of the educational environment are covered by these surveys, such as teaching methods, school resources, and socioeconomic position. PISA offers a thorough grasp of the variables affecting student outcomes by combining contextual data with academic performance. This comprehensive method enables focused interventions to enhance teaching and learning worldwide by assisting researchers, policymakers, and educators in identifying critical factors that support or impede educational performance.
[1] "year" "country" "school_id" "student_id" "mother_educ"
[6] "father_educ" "gender" "computer" "internet" "math"
[11] "read" "science" "stu_wgt" "desk" "room"
[16] "dishwasher" "television" "computer_n" "laptop_n" "car"
[21] "book" "wealth" "escs" "curiosity" The student dataset includes the following columns: year, country, school_id, student_id, mother_educ, father_educ, gender, computer, internet, math, read, science, stu_wgt, desk, room, dishwasher, television, computer_n, laptop_n, car, book, wealth, escs, and curiosity. These columns provide comprehensive details about the students’ background, academic performance, and access to resources, offering a robust dataset for analysis of educational outcomes and socio-economic factors.
With the gender difference in average maths scores (measured as girls’ scores - boys’ scores) on the x-axis, this graphic displays the gender gap analysis in mathematics across several nations. The y-axis lists the countries, and the lines indicate confidence intervals, and each point displays the average score difference. Grey points indicate no discernible gender difference, red points emphasise nations where girls outperform boys, and blue points indicate nations where boys exceed girls. The graph illustrates the different degrees of gender inequality in maths ability, with boys outperforming girls in many nations and the opposite tendency in a small number.
An analysis of the gender gap in reading scores across several nations is shown in this graph. The gender gap in average reading scores is shown by the x-axis, which is computed as (Girls’ scores - Boys’ scores). The lines display the bootstrap confidence intervals, and the y-axis lists the nations. Each point on the y-axis reflects the average gender gap in reading performance. The red dots and lines illustrate that, in the majority of countries, girls perform significantly better than boys in reading, with scores veering towards positive values. The global pattern where girls tend to score higher on reading examinations is highlighted by the vertical zero line, which indicates no difference, and the fact that few countries display boys outperforming girls in reading.
This graph presents a Gender Gap Analysis in science scores across various countries, showing the difference between girls’ and boys’ average science scores. The x-axis represents the gender difference, calculated as( Girl’s scores - Boy’s Scores), while the y-axis lists the countries. The red points and lines indicate that girls outperform boys in science in several countries, while blue points and lines indicate that boys outperform girls. Grey points and lines represent countries where there is no significant gender difference. The vertical line at zero shows no difference, making it easy to see that in most countries, girls tend to perform better than boys in science, as shown by the positive values on the right side of the chart.